Landfills in Sub-Saharan Africa A Case Study of Ghana and Kenya

4. Results The results of the study are presented in this section. The results are divided into two parts: the first part presents the results of the analysis of the data collected from the landfills in -Ghana and and the second part -Kenya.

For Ghana when we did Two way fixed effects model, we found that the coefficient of the Treatment variable is -0.065, although not statistically significant.The negative coefficient can be still interpreted as there is a negative impact on wealth if household is near the landfill i.e it belongs to the treatment group. But as the result is not statistically significant, we cannot come to any conclusion.

library(here)

Warning: package 'here' was built under R version 4.3.3

here() starts at C:/Users/laban/Downloads/Empirical_Research

library(modelsummary)

Warning: package 'modelsummary' was built under R version 4.3.3

`modelsummary` 2.0.0 now uses `tinytable` as its default table-drawing
  backend. Learn more at: https://vincentarelbundock.github.io/tinytable/

Revert to `kableExtra` for one session:

  options(modelsummary_factory_default = 'kableExtra')
  options(modelsummary_factory_latex = 'kableExtra')
  options(modelsummary_factory_html = 'kableExtra')

Silence this message forever:

  config_modelsummary(startup_message = FALSE)

Reg_tableGhanaTWFE <- readRDS(here("output/Reg_tableGhanaTWFE.rds"))

Reg_tableGhanaTWFE|> modelsummary()

	(1)
Treatment	-0.065
	(0.126)
Num.Obs.	7232
R2	0.020
R2 Adj.	0.019
R2 Within	0.002
R2 Within Adj.	0.002
AIC	14308.8
BIC	14336.3
RMSE	0.65
Std.Errors	by: LANDFILLS
FE: LANDFILLS	X
FE: Year	X

We also performed only landfill Fixed effect for Ghana(to make it comparable for Kenya).The coefficient of interest came to be -0.0531, although again not statistically significant.

Reg_tableGhana <- readRDS(here("output/Reg_tableGhana.rds"))

Reg_tableGhana|> modelsummary()

	(1)
Treatment	-0.053
	(0.172)
Num.Obs.	3428
R2	0.004
R2 Adj.	0.003
R2 Within	0.001
R2 Within Adj.	0.001
AIC	6848.5
BIC	6866.9
RMSE	0.66
Std.Errors	by: LANDFILLS
FE: LANDFILLS	X

For Kenya as mentioned earlier, we had data for one year 2014. As a result we could perform only landfill fixed effect model. The coefficient of interest came to be 0.1559. Although again not statistically significant, but here it shows a positive impact on wealth if household is near the landfill i.e it belongs to the treatment group. This result is quite opposite to what we got for Ghana.

Reg_tableKenya <- readRDS(here("output/Reg_tableKenya.rds"))

Reg_tableKenya|> modelsummary()

	(1)
Treatment	0.156
	(0.166)
Num.Obs.	4762
R2	0.014
R2 Adj.	0.013
R2 Within	0.011
R2 Within Adj.	0.011
AIC	10543.8
BIC	10569.6
RMSE	0.73
Std.Errors	by: LANDFILLS
FE: LANDFILLS	X